Forecast modeling of gas utilization rate of blast furnace based on support vector machine
JIANG De-wen1, WANG Zhen-yang1, DAI Jian-hua1, ZHOU Xin-fu1, WANG Xin-dong2, ZHANG Jian-liang1,3
1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. Iron and Steel Technology Research Institute, Hegang Group Co., Ltd., Tangshan 063000, Hebei, China; 3. School of Chemical Engineering, The University of Queensland, St Lucia QLD 4072, Australia
Abstract：Gas utilization rate (GUR) is an important indicator of blast furnace (BF) for reflecting the energy consumption and smooth operation of BF. In order to achieve accurate prediction of GUR, the appropriate input parameters are selected based on the maximum information coefficient. The gas utilization rates after one hour and two hours of the state parameter time are selected as output parameters, respectively. At the same time, it is essential for the model to standardize the data of BF. On this basis, a prediction model of GUR based on support vector regression (SVR) is established, and part of the production data of the BF is used to compare the prediction results of the model with those of the multi-layer perceptron (MLP) model. Final result demonstrates the SVR model is more accurate in predicting the GUR after one hour and two hours and achieves better prediction results.
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2021, Vol. 31 
